Introduction

  • Keep in mind the tradeoffs based on speed, explainability, simplicity & performance
  • Bike sharing data is found on Kaggle
  • Potential things to explore:
    • Duration of travel, departure and arrival position
    • Casual vs. registered rental wrt weather, time, etc.
    • Understand seasonality / time series analysis
    • Predict number of rentals on a day
    • Event and Anomaly Detection

Data

Sample

options(scipen=999)
seed = 2018
pkgs = c('data.table', 'magrittr', 'caret', 'glmnet', 'ranger', 'plotly', 'iml')
inst = lapply(pkgs, library, character.only=T, quietly=T); rm(pkgs, inst)
source('~/Documents/R/Utils/functions/summarize.R')
source('~/Documents/R/Utils/functions/one_hot.R')
source('~/Documents/R/Utils/functions/model_auc.R')
source('~/Documents/R/Utils/functions/feature_comparison.R')
source('~/Documents/R/Utils/functions/feature_distribution.R')
source('~/Documents/R/Utils/functions/helper.R')
day = fread("~/Rrrrr/projects/bike_sharing/day.csv") %>%
  .[, dteday := as.Date(dteday)]
hour = fread("~/Rrrrr/projects/bike_sharing/hour.csv") %>%
  .[, dteday := as.Date(dteday)]
head(day)

Summarize

The goal is to find variables with:

  • missing values
  • low variance
  • outliers – casual users shows the most diverse behavior

Day

sum.day = Summarize(day); DT::datatable(sum.day, options = list(dom = "tip"))

Hour

sum.hour = Summarize(hour); DT::datatable(sum.hour, options = list(dom = "tip"))

Learnings

  • Holiday is only marked for weekdays
day[,.N,.(weekday, workingday, holiday)]

Anomaly detection EDA

  • Given condition, actual rental is way higher than predicted rentals, especially casual renters on days of events
  • Time of rental – might be a peak at certain time
  • My guess is holiday is more likely to have events – only 21 holidays
  • Holiday & weekends people might use bikes on a longer stretch of time
  • Clustering on raw features & on shapley values

Feature Density

FeatureComparison(list(holiday = day[holiday==1, cnt], 
                       weekday = day[holiday==0 & workingday==1, cnt],
                       weekend = day[holiday==0 & workingday==0, cnt]),
                  vectorName = "Total rental", sample = F, numerical = T)

FeatureComparison(list(holiday = day[holiday==1, casual], 
                       weekday = day[holiday==0 & workingday==1, casual],
                       weekend = day[holiday==0 & workingday==0, casual]),
                  vectorName = "Casual rental", sample = F, numerical = T)

FeatureComparison(list(holiday = day[holiday==1, registered], 
                       weekday = day[holiday==0 & workingday==1, registered],
                       weekend = day[holiday==0 & workingday==0, registered]),
                  vectorName = "Registered rental", sample = T, numerical = T)

Feature Over Time

  • 2012-10-29 & 2012-10-30 why is it so low?
    • Hurricane Sandy
FeatureDistribution(list(weekday = day[holiday==0 & workingday==1],
                         weekend = day[holiday==0 & workingday==0],
                         holiday = day[holiday==1], overall = day),
                    featureName = "cnt", periodName = "dteday")

  • Casual users show the most diverse behaviors between holiday, weekends & weekdays
  • 2011-07-04 is very high – 4th of July!
FeatureDistribution(list(weekday = day[holiday==0 & workingday==1],
                         weekend = day[holiday==0 & workingday==0],
                         holiday = day[holiday==1], overall = day),
                    featureName = "casual", periodName = "dteday")

  • Looks like registered users like to use more during weekdays, which makes sense as the registered users use bikes as work commute transportation
FeatureDistribution(list(weekday = day[holiday==0 & workingday==1],
                         weekend = day[holiday==0 & workingday==0],
                         holiday = day[holiday==1], overall = day),
                    featureName = "registered", periodName = "dteday")

Weather

  • Casual users like to bike on cooler days during summer, and clearer days in spring & fall
FeatureDistribution(list(Clear = day[weathersit==1], 
                         Mist = day[weathersit==2],
                         Light = day[weathersit==3]),
                    featureName = "casual", periodName = "mnth")

  • Again, weather has less effect on registered users (similar to weekdays)
FeatureDistribution(list(Clear = day[weathersit==1], 
                         Mist = day[weathersit==2],
                         Light = day[weathersit==3]),
                    featureName = "registered", periodName = "mnth")

Day of week

  • Casual rental: Saturdays & Sundays are heavy days
FeatureComparison(list(S = day[weekday==0, casual], 
                       M = day[weekday==1, casual],
                       T = day[weekday==2, casual],
                       W = day[weekday==3, casual],
                       Th = day[weekday==4, casual],
                       F = day[weekday==5, casual],
                       St = day[weekday==6, casual]),
                  vectorName = "Casual rental", sample = F, numerical = T)

  • Registered rental: Wednesdays & Thursdays are heavy days
FeatureComparison(list(S = day[weekday==0, registered], 
                       M = day[weekday==1, registered],
                       T = day[weekday==2, registered],
                       W = day[weekday==3, registered],
                       Th = day[weekday==4, registered],
                       F = day[weekday==5, registered],
                       St = day[weekday==6, registered]),
                  vectorName = "Registered rental", sample = F, numerical = T)

Time

  • Starting from 0, every 15\(^\circ\) is one hour; so 120\(^\circ\) means 8am and 255\(^\circ\) means 5pm
  • How far the dot is from origin is the average number of users during that time

  • Casual users
    • Rentals on Saturday > Sunday > Weekdays
    • More rentals in the afternoon on weekends
    • Most weekday rental at ~5pm
  • Registered users
    • More rentals during weekdays > weekends, Saturday & Sunday uses are not very different
    • Peak hours are 8am, 5pm & 6pm during weekdays
time = hour[workingday==1, .(day = "Weekday", c = mean(casual), r = mean(registered)), hr] %>%
  rbind(hour[weekday==6, .(day = "Sat", c = mean(casual), r = mean(registered)), hr]) %>%
  rbind(hour[weekday==0, .(day = "Sun", c = mean(casual), r = mean(registered)), hr]) %>%
  .[, deg := hr*15] %>%
  .[, let := LETTERS[hr+1]]
# plot_ly(time, x=~hr, y=~cnt, color = ~day) 
p <- plot_ly(type = 'scatterpolar', mode = 'markers', 
             r = time[day=="Weekday"]$c,
             theta = time[day=="Weekday"]$deg,
             name = "Casual_Week", marker = list(opacity = 0.5)) %>%
  add_trace(type = 'scatterpolar', mode = 'markers', 
            r = time[day=="Sat"]$c,
            theta = time[day=="Sat"]$deg,
            name = "Casual_Sat", marker = list(opacity = 0.5)) %>%
  add_trace(type = 'scatterpolar', mode = 'markers', 
            r = time[day=="Sun"]$c,
            theta = time[day=="Sun"]$deg,
            name = "Casual_Sun", marker = list(opacity = 0.5)) %>%
  add_trace(type = 'scatterpolar', mode = 'markers', 
            r = time[day=="Weekday"]$r,
            theta = time[day=="Weekday"]$deg,
            name = "Reg_Week",
            marker = list(opacity = 0.5)) %>%
  add_trace(type = 'scatterpolar', mode = 'markers', 
            r = time[day=="Sat"]$r,
            theta = time[day=="Sat"]$deg,
            name = "Reg_Sat",
            marker = list(opacity = 0.5)) %>%
  add_trace(type = 'scatterpolar', mode = 'markers', 
            r = time[day=="Sun"]$r,
            theta = time[day=="Sun"]$deg,
            name = "Reg_Sun",
            marker = list(opacity = 0.5)) %>%
  layout(polar = list(radialaxis = list(angle = 0),
                      angularaxis = list(direction = 'clockwise', dtick = 15)))
p

Holiday

  • 21 Holidays – easy to spot days with events
day[,.N, .(holiday, workingday)]
day[holiday==1,.(dteday, yr, weekday, casual, registered, cnt)]

  • Time
hour[dteday=="2011-07-04",.(hr, weathersit, temp, atemp, hum, windspeed, casual, registered, cnt)]

Regression

Train-test split

  • Time split
cutoff = "2012-09-01"
# train-test split by time
data.train = hour[dteday<cutoff, -c("instant", "dteday", "casual", "registered")]
week.train = hour[dteday<cutoff] %>% .[, week := week(dteday)] %>%
  .[, -c("instant", "dteday", "casual", "registered")]
date.train = hour[dteday<cutoff] %>% .[, week := week(dteday)] %>%
  .[, date := mday(dteday)] %>%
  .[, -c("instant", "dteday", "casual", "registered")]
cas.train = hour[dteday<cutoff]$casual
reg.train = hour[dteday<cutoff]$registered
data.test = hour[dteday>=cutoff, -c("instant", "dteday", "casual", "registered")]
week.test = hour[dteday>=cutoff] %>% .[, week := week(dteday)] %>%
  .[, -c("instant", "dteday", "casual", "registered")]
date.test = hour[dteday>=cutoff] %>% .[, week := week(dteday)] %>%
  .[, date := mday(dteday)] %>%
  .[, -c("instant", "dteday", "casual", "registered")]
# check to make sure distribution is consistent
FeatureDistribution(list(Train = hour[dteday<cutoff], 
                         Test = hour[dteday>=cutoff]),
                    featureName = "cnt", periodName = "dteday")

One-hot-encoding

  • Season & weekday
hour.ohot = copy(hour)
OneHot(hour.ohot, "mnth")
OneHot(hour.ohot, "hr")
OneHot(hour.ohot, "season")
OneHot(hour.ohot, "weekday")
OneHot(hour.ohot, "weathersit")
ohot.train = hour.ohot[dteday<cutoff, -c("instant", "dteday", "casual", "registered")]
ohot.test = hour.ohot[dteday>=cutoff, -c("instant", "dteday", "casual", "registered")]

Additional work

  • Identify & remove outliers

Model build

  • Train with entire dataset
  • Train with events (0/1) column
  • Train separate models for registered vs. casual users
    • Exact
  • Evaluation metric determines how good a model is, for e.g. RMSE means predicted values are in a narrow band in comparison to MAE

GLM

# 10-fold CV
set.seed(seed)
glm.ctr <- trainControl(method = "repeatedcv", number = 10, repeats = 5)
# search grid
glm.grd <-  expand.grid(alpha = 0:10/10, lambda = 10^seq(3,-6))
# model                        
set.seed(seed)
glm.all <- train(x = ohot.train[,-"cnt"], y = ohot.train$cnt, 
                 method = "glmnet", trControl = glm.ctr, tuneGrid = glm.grd)
There were missing values in resampled performance measures.
plot(glm.all)


glm.all$bestTune

  • RMSE = 132.4112055
  • RMSE results in conservative predictions – predicted values are all < 600
pred.glm = data.table(actual = ohot.test$cnt, 
                      pred = predict(glm.all, newdata = ohot.test))
plot_ly(pred.glm, x = ~actual, y = ~pred) %>%
  add_markers() %>%
  add_lines(x=c(1,800), y=c(1,800), showlegend = F)

  • In comparison, RMSE on train = 95.1339412
  • GLM has a higher bias
pred.glm.train = data.table(actual = ohot.train$cnt, 
                            pred = predict(glm.all, newdata = ohot.train))
plot_ly(pred.glm.train, x = ~actual, y = ~pred) %>%
  add_markers() %>%
  add_lines(x=c(1,800), y=c(1,800), showlegend = F)

GLM(2models)

Casual model
# model                        
set.seed(seed)
glm.cas <- train(x = ohot.train[,-"cnt"], y = cas.train, 
                 method = "glmnet", trControl = glm.ctr, tuneGrid = glm.grd)
There were missing values in resampled performance measures.
plot(glm.cas)


glm.cas$bestTune

Registered model
set.seed(seed)
glm.reg <- train(x = ohot.train[,-"cnt"], y = reg.train, 
                 method = "glmnet", trControl = glm.ctr, tuneGrid = glm.grd)
There were missing values in resampled performance measures.
plot(glm.reg)


glm.reg$bestTune

Performance
glm.two = data.table(hour[dteday>=cutoff, .(actual = cnt, dteday)],
                      cas = predict(glm.cas, newdata = data.fe)[14492:17379],
                      reg = predict(glm.reg, newdata = data.fe)[14492:17379]) %>%
  .[, pred := cas + reg]
Error in cbind2(1, newx) %*% nbeta : 
  Cholmod error 'X and/or Y have wrong dimensions' at file ../MatrixOps/cholmod_sdmult.c, line 90

FeatureComparison(list(glm.two$actual, glm.two$pred))

  • Average hourly prediction
FeatureDistribution(list(Train = hour[dteday<cutoff,.(dteday, group = "train", value = cnt)],
                         Test = glm.two[,.(dteday, group = "actual", value = actual)],
                         Preds = glm.two[,.(dteday, group = "pred", value = pred)]), 
                    featureName = "value", periodName = "dteday")

XGB

  • nrounds (# Boosting Iterations), max_depth (Max Tree Depth), eta (Shrinkage), gamma (Minimum Loss Reduction), colsample_bytree (Subsample Ratio of Columns), min_child_weight (Minimum Sum of Instance Weight), subsample
# 10-fold CV
set.seed(seed)
xgb.ctr <- trainControl(method = "repeatedcv", number = 10, repeats = 1)
# search grid
# glm.grd <-  expand.grid(alpha = 0:10/10, lambda = 10^seq(3,-6))
# model                        
set.seed(seed)
xgb.all <- train(x = data.train[,-"cnt"], y = data.train$cnt, 
                 method = "xgbTree", trControl = xgb.ctr)
plot(xgb.all)


xgb.all$bestTune

Performance
  • RMSE = 79.3423604
pred.xgb = data.table(actual = data.test$cnt, 
                      pred = predict(xgb.all, newdata = data.test))
plot_ly(pred.xgb, x = ~actual, y = ~pred) %>%
  add_markers() %>%
  add_lines(x=c(1,800), y=c(1,800), showlegend = F)

  • In comparison, RMSE on train = 45.8525654
pred.xgb.train = data.table(actual = data.train$cnt, 
                            pred = predict(xgb.all, newdata = data.train))
plot_ly(pred.xgb.train, x = ~actual, y = ~pred) %>%
  add_markers() %>%
  add_lines(x=c(1,800), y=c(1,800), showlegend = F)

XGB(2models)

  • Slightly performance as the normal XGB model
Casual model
set.seed(seed)
xgb.cas <- train(x = data.train[,-"cnt"], y = cas.train, 
                 method = "xgbTree", trControl = xgb.ctr)
plot(xgb.cas)


xgb.cas$bestTune

Registered model
set.seed(seed)
xgb.reg <- train(x = data.train[,-"cnt"], y = reg.train, 
                 method = "xgbTree", trControl = xgb.ctr)
plot(xgb.reg)


xgb.reg$bestTune

Performance
  • RMSE = 77.6327841
pred.two = data.table(hour[dteday>=cutoff, .(actual = cnt, dteday)],
                      cas = predict(xgb.cas, newdata = data.test),
                      reg = predict(xgb.reg, newdata = data.test)) %>%
  .[, pred := cas + reg]
plot_ly(pred.two, x = ~actual, y = ~pred) %>%
  add_markers() %>%
  add_lines(x=c(1,800), y=c(1,800), showlegend = F)

FeatureComparison(list(pred.two$actual, pred.two$pred))

  • Average hourly prediction
FeatureDistribution(list(Train = hour[dteday<cutoff,.(dteday, group = "train", value = cnt)],
                         Test = pred.two[,.(dteday, group = "actual", value = actual)],
                         Preds = pred.two[,.(dteday, group = "pred", value = pred)]), 
                    featureName = "value", periodName = "dteday")

XGB(week)

# 10-fold CV
set.seed(seed)
xgb.ctr <- trainControl(method = "repeatedcv", number = 10, repeats = 1)
# search grid
# glm.grd <-  expand.grid(alpha = 0:10/10, lambda = 10^seq(3,-6))
# model                        
set.seed(seed)
xgb.week <- train(x = week.train[,-"cnt"], y = week.train$cnt, 
                 method = "xgbTree", trControl = xgb.ctr)
plot(xgb.week)


xgb.week$bestTune

  • RMSE = 78.897122
week.xgb = data.table(hour[dteday>=cutoff, .(actual = cnt, dteday)],
                      pred = predict(xgb.week, newdata = week.test))
plot_ly(week.xgb, x = ~actual, y = ~pred) %>%
  add_markers() %>%
  add_lines(x=c(1,800), y=c(1,800), showlegend = F)

  • Average hourly prediction
FeatureDistribution(list(Train = hour[dteday<cutoff,.(dteday, group = "train", value = cnt)],
                         Test = week.xgb[,.(dteday, group = "actual", value = actual)],
                         Preds = week.xgb[,.(dteday, group = "pred", value = pred)]), 
                    featureName = "value", periodName = "dteday")

XGB(date)

set.seed(seed)
xgb.date <- train(x = date.train[,-"cnt"], y = date.train$cnt, 
                 method = "xgbTree", trControl = xgb.ctr)
plot(xgb.date)


xgb.date$bestTune

  • RMSE = 77.7874697
date.xgb = data.table(hour[dteday>=cutoff, .(actual = cnt, dteday)],
                      pred = predict(xgb.date, newdata = date.test))
plot_ly(date.xgb, x = ~actual, y = ~pred) %>%
  add_markers() %>%
  add_lines(x=c(1,800), y=c(1,800), showlegend = F)

  • Average hourly prediction
FeatureDistribution(list(Train = hour[dteday<cutoff,.(dteday, group = "train", value = cnt)],
                         Test = date.xgb[,.(dteday, group = "actual", value = actual)],
                         Preds = date.xgb[,.(dteday, group = "pred", value = pred)]), 
                    featureName = "value", periodName = "dteday")

XGB(no mnth)

set.seed(seed)
xgb.mth <- train(x = data.train[,-c("mnth","cnt")], y = data.train$cnt, 
                 method = "xgbTree", trControl = xgb.ctr)
plot(xgb.mth)


xgb.mth$bestTune

Performance
  • RMSE = 79.588598
mth.xgb = data.table(hour[dteday>=cutoff, .(actual = cnt, dteday)],
                     pred = predict(xgb.mth, newdata = data.test))
plot_ly(mth.xgb, x = ~actual, y = ~pred) %>%
  add_markers() %>%
  add_lines(x=c(1,800), y=c(1,800), showlegend = F)

  • In comparison, RMSE on train = 46.5857025
mth.xgb.train = data.table(actual = data.train$cnt, 
                           pred = predict(xgb.mth, newdata = data.train))
plot_ly(mth.xgb.train, x = ~actual, y = ~pred) %>%
  add_markers() %>%
  add_lines(x=c(1,800), y=c(1,800), showlegend = F)

Performance

FeatureDistribution(list(Train = hour[dteday<cutoff,.(dteday, group = "train", value = cnt)],
                         Test = date.xgb[,.(dteday, group = "actual", value = actual)],
                         NoMth = mth.xgb[,.(dteday, group = "pred", value = pred)],
                         XGB2 = pred.two[,.(dteday, group = "pred", value = pred)],
                         Week = week.xgb[,.(dteday, group = "pred", value = pred)],
                         Date = date.xgb[,.(dteday, group = "pred", value = pred)]), 
                    featureName = "value", periodName = "dteday")

Explain

#calc.relimp

save.image(file = "bike.RData")
load("bike.RData")
---
title: "Bike Sharing"
output: 
  html_notebook:
    code_folding: hide
    highlight: tango
    theme: flatly
    toc: yes
    toc_float:
      collapsed: yes
---

## Introduction

* Keep in mind the tradeoffs based on speed, explainability, simplicity & performance
* Bike sharing data is found on [Kaggle](https://www.kaggle.com/marklvl/bike-sharing-dataset/home)
* Potential things to explore: 
    + Duration of travel, departure and arrival position
    + Casual vs. registered rental wrt weather, time, etc.
    + Understand seasonality / time series analysis
    + Predict number of rentals on a day
    + Event and Anomaly Detection
    
*****
## Data
### Sample
```{r, message=FALSE, warning=FALSE}
options(scipen=999)
seed = 2018
pkgs = c('data.table', 'magrittr', 'caret', 'glmnet', 'ranger', 'plotly', 'iml')
inst = lapply(pkgs, library, character.only=T, quietly=T); rm(pkgs, inst)
source('~/Documents/R/Utils/functions/summarize.R')
source('~/Documents/R/Utils/functions/one_hot.R')
source('~/Documents/R/Utils/functions/model_auc.R')
source('~/Documents/R/Utils/functions/feature_comparison.R')
source('~/Documents/R/Utils/functions/feature_distribution.R')
source('~/Documents/R/Utils/functions/helper.R')
day = fread("~/Rrrrr/projects/bike_sharing/day.csv") %>%
  .[, dteday := as.Date(dteday)]
hour = fread("~/Rrrrr/projects/bike_sharing/hour.csv") %>%
  .[, dteday := as.Date(dteday)]
head(day)
```

*****
### Summarize {.tabset}

The goal is to find variables with: 

* missing values
* low variance
* outliers -- casual users shows the most diverse behavior
   
#### Day 
```{r}
sum.day = Summarize(day); DT::datatable(sum.day, options = list(dom = "tip"))
```

*****
#### Hour
```{r}
sum.hour = Summarize(hour); DT::datatable(sum.hour, options = list(dom = "tip"))
```
*****
### Learnings

* Holiday is only marked for weekdays
```{r}
day[,.N,.(weekday, workingday, holiday)]
```

*****
## Anomaly detection EDA {.tabset}

* Given condition, actual rental is way higher than predicted rentals, especially casual renters on days of events
* Time of rental -- might be a peak at certain time
* My guess is holiday is more likely to have events -- only 21 holidays
* Holiday & weekends people might use bikes on a longer stretch of time
* Clustering on raw features & on shapley values

### Feature Density
```{r, fig.width=8, fig.height=4}
FeatureComparison(list(holiday = day[holiday==1, cnt], 
                       weekday = day[holiday==0 & workingday==1, cnt],
                       weekend = day[holiday==0 & workingday==0, cnt]),
                  vectorName = "Total rental", sample = F, numerical = T)
```

*****
```{r, fig.width=8, fig.height=4}
FeatureComparison(list(holiday = day[holiday==1, casual], 
                       weekday = day[holiday==0 & workingday==1, casual],
                       weekend = day[holiday==0 & workingday==0, casual]),
                  vectorName = "Casual rental", sample = F, numerical = T)
```

*****
```{r, fig.width=8, fig.height=4}
FeatureComparison(list(holiday = day[holiday==1, registered], 
                       weekday = day[holiday==0 & workingday==1, registered],
                       weekend = day[holiday==0 & workingday==0, registered]),
                  vectorName = "Registered rental", sample = T, numerical = T)
```

### Feature Over Time

* `2012-10-29` & `2012-10-30` why is it so low?
    + Hurricane Sandy
```{r, fig.width=8, fig.height=4}
FeatureDistribution(list(weekday = day[holiday==0 & workingday==1],
                         weekend = day[holiday==0 & workingday==0],
                         holiday = day[holiday==1], overall = day),
                    featureName = "cnt", periodName = "dteday")
```

*****

* Casual users show the most diverse behaviors between holiday, weekends & weekdays
* `2011-07-04` is very high -- 4th of July!
```{r, fig.width=8, fig.height=4}
FeatureDistribution(list(weekday = day[holiday==0 & workingday==1],
                         weekend = day[holiday==0 & workingday==0],
                         holiday = day[holiday==1], overall = day),
                    featureName = "casual", periodName = "dteday")
```

*****

* Looks like registered users like to use more during weekdays, which makes sense as the registered users use bikes as work commute transportation
```{r, fig.width=8, fig.height=4}
FeatureDistribution(list(weekday = day[holiday==0 & workingday==1],
                         weekend = day[holiday==0 & workingday==0],
                         holiday = day[holiday==1], overall = day),
                    featureName = "registered", periodName = "dteday")
```

*****
### Weather

* Casual users like to bike on cooler days during summer, and clearer days in spring & fall
```{r, fig.width=8, fig.height=4}
FeatureDistribution(list(Clear = day[weathersit==1], 
                         Mist = day[weathersit==2],
                         Light = day[weathersit==3]),
                    featureName = "casual", periodName = "mnth")
```

*****

* Again, weather has less effect on registered users (similar to weekdays)
```{r, fig.width=8, fig.height=4}
FeatureDistribution(list(Clear = day[weathersit==1], 
                         Mist = day[weathersit==2],
                         Light = day[weathersit==3]),
                    featureName = "registered", periodName = "mnth")
```

*****
### Day of week

* Casual rental: Saturdays & Sundays are heavy days
```{r, fig.width=8, fig.height=4}
FeatureComparison(list(S = day[weekday==0, casual], 
                       M = day[weekday==1, casual],
                       T = day[weekday==2, casual],
                       W = day[weekday==3, casual],
                       Th = day[weekday==4, casual],
                       F = day[weekday==5, casual],
                       St = day[weekday==6, casual]),
                  vectorName = "Casual rental", sample = F, numerical = T)
```

*****

* Registered rental: Wednesdays & Thursdays are heavy days
```{r, fig.width=8, fig.height=4}
FeatureComparison(list(S = day[weekday==0, registered], 
                       M = day[weekday==1, registered],
                       T = day[weekday==2, registered],
                       W = day[weekday==3, registered],
                       Th = day[weekday==4, registered],
                       F = day[weekday==5, registered],
                       St = day[weekday==6, registered]),
                  vectorName = "Registered rental", sample = F, numerical = T)
```

*****

### Time 

* Starting from 0, every 15$^\circ$ is one hour; so 120$^\circ$ means 8am and 255$^\circ$ means 5pm
* How far the dot is from origin is the average number of users during that time

* Casual users
    + Rentals on Saturday > Sunday > Weekdays
    + More rentals in the afternoon on weekends
    + Most weekday rental at ~5pm
* Registered users
    + More rentals during weekdays > weekends, Saturday & Sunday uses are not very different
    + Peak hours are 8am, 5pm & 6pm during weekdays
```{r, fig.width=7}
time = hour[workingday==1, .(day = "Weekday", c = mean(casual), r = mean(registered)), hr] %>%
  rbind(hour[weekday==6, .(day = "Sat", c = mean(casual), r = mean(registered)), hr]) %>%
  rbind(hour[weekday==0, .(day = "Sun", c = mean(casual), r = mean(registered)), hr]) %>%
  .[, deg := hr*15] %>%
  .[, let := LETTERS[hr+1]]
# plot_ly(time, x=~hr, y=~cnt, color = ~day) 
p <- plot_ly(type = 'scatterpolar', mode = 'markers', 
             r = time[day=="Weekday"]$c,
             theta = time[day=="Weekday"]$deg,
             name = "Casual_Week", marker = list(opacity = 0.5)) %>%
  add_trace(type = 'scatterpolar', mode = 'markers', 
            r = time[day=="Sat"]$c,
            theta = time[day=="Sat"]$deg,
            name = "Casual_Sat", marker = list(opacity = 0.5)) %>%
  add_trace(type = 'scatterpolar', mode = 'markers', 
            r = time[day=="Sun"]$c,
            theta = time[day=="Sun"]$deg,
            name = "Casual_Sun", marker = list(opacity = 0.5)) %>%
  add_trace(type = 'scatterpolar', mode = 'markers', 
            r = time[day=="Weekday"]$r,
            theta = time[day=="Weekday"]$deg,
            name = "Reg_Week",
            marker = list(opacity = 0.5)) %>%
  add_trace(type = 'scatterpolar', mode = 'markers', 
            r = time[day=="Sat"]$r,
            theta = time[day=="Sat"]$deg,
            name = "Reg_Sat",
            marker = list(opacity = 0.5)) %>%
  add_trace(type = 'scatterpolar', mode = 'markers', 
            r = time[day=="Sun"]$r,
            theta = time[day=="Sun"]$deg,
            name = "Reg_Sun",
            marker = list(opacity = 0.5)) %>%
  layout(polar = list(radialaxis = list(angle = 0),
                      angularaxis = list(direction = 'clockwise', dtick = 15)))

p
```

*****
### Holiday

* 21 Holidays -- easy to spot days with events
```{r}
day[,.N, .(holiday, workingday)]
```

```{r}
day[holiday==1,.(dteday, yr, weekday, casual, registered, cnt)]
```

*****
* Time
```{r}
hour[dteday=="2011-07-04",.(hr, weathersit, temp, atemp, hum, windspeed, casual, registered, cnt)]
```

*****
## Regression
### Train-test split

* Time split
```{r, fig.width=8, fig.height=4}
cutoff = "2012-09-01"
# train-test split by time
data.train = hour[dteday<cutoff, -c("instant", "dteday", "casual", "registered")]
week.train = hour[dteday<cutoff] %>% .[, week := week(dteday)] %>%
  .[, -c("instant", "dteday", "casual", "registered")]
date.train = hour[dteday<cutoff] %>% .[, week := week(dteday)] %>%
  .[, date := mday(dteday)] %>%
  .[, -c("instant", "dteday", "casual", "registered")]
cas.train = hour[dteday<cutoff]$casual
reg.train = hour[dteday<cutoff]$registered
data.test = hour[dteday>=cutoff, -c("instant", "dteday", "casual", "registered")]
week.test = hour[dteday>=cutoff] %>% .[, week := week(dteday)] %>%
  .[, -c("instant", "dteday", "casual", "registered")]
date.test = hour[dteday>=cutoff] %>% .[, week := week(dteday)] %>%
  .[, date := mday(dteday)] %>%
  .[, -c("instant", "dteday", "casual", "registered")]
# check to make sure distribution is consistent
FeatureDistribution(list(Train = hour[dteday<cutoff], 
                         Test = hour[dteday>=cutoff]),
                    featureName = "cnt", periodName = "dteday")
```

*****
### One-hot-encoding

* Season & weekday
```{r}
hour.ohot = copy(hour)
OneHot(hour.ohot, "mnth")
OneHot(hour.ohot, "hr")
OneHot(hour.ohot, "season")
OneHot(hour.ohot, "weekday")
OneHot(hour.ohot, "weathersit")
ohot.train = hour.ohot[dteday<cutoff, -c("instant", "dteday", "casual", "registered")]
ohot.test = hour.ohot[dteday>=cutoff, -c("instant", "dteday", "casual", "registered")]
```

*****
### Additional work

* Identify & remove outliers
```{r}

```

*****
### Model build {.tabset}

* Train with entire dataset
* Train with events (`0`/`1`) column
* Train separate models for registered vs. casual users
    + Exact
* Evaluation metric determines how good a model is, for e.g. RMSE means predicted values are in a narrow band in comparison to MAE

#### GLM
```{r}
# 10-fold CV
set.seed(seed)
glm.ctr <- trainControl(method = "repeatedcv", number = 10, repeats = 5)
# search grid
glm.grd <-  expand.grid(alpha = 0:10/10, lambda = 10^seq(3,-6))
# model                        
set.seed(seed)
glm.all <- train(x = ohot.train[,-"cnt"], y = ohot.train$cnt, 
                 method = "glmnet", trControl = glm.ctr, tuneGrid = glm.grd)
plot(glm.all)
```

*****
```{r}
glm.all$bestTune
```

*****

* RMSE = `r pred.glm[, mean((actual - pred)^2)] %>% sqrt`
* RMSE results in conservative predictions -- predicted values are all < 600
```{r, fig.width=8, fig.height=4}
pred.glm = data.table(actual = ohot.test$cnt, 
                      pred = predict(glm.all, newdata = ohot.test))
plot_ly(pred.glm, x = ~actual, y = ~pred) %>%
  add_markers() %>%
  add_lines(x=c(1,800), y=c(1,800), showlegend = F)
```

*****

* In comparison, RMSE on train = `r pred.glm.train[, mean((actual - pred)^2)] %>% sqrt`
* GLM has a higher bias
```{r, fig.width=8, fig.height=4}
pred.glm.train = data.table(actual = ohot.train$cnt, 
                            pred = predict(glm.all, newdata = ohot.train))
plot_ly(pred.glm.train, x = ~actual, y = ~pred) %>%
  add_markers() %>%
  add_lines(x=c(1,800), y=c(1,800), showlegend = F)
```

*****
#### GLM(2models) {.tabset}
##### Casual model
```{r}
# model                        
set.seed(seed)
glm.cas <- train(x = ohot.train[,-"cnt"], y = cas.train, 
                 method = "glmnet", trControl = glm.ctr, tuneGrid = glm.grd)
plot(glm.cas)
```

*****
```{r}
glm.cas$bestTune
```

*****
##### Registered model
```{r}
set.seed(seed)
glm.reg <- train(x = ohot.train[,-"cnt"], y = reg.train, 
                 method = "glmnet", trControl = glm.ctr, tuneGrid = glm.grd)
plot(glm.reg)
```

*****
```{r}
glm.reg$bestTune
```

*****
##### Performance

```{r, fig.width=8, fig.height=4}
# * RMSE = `r glm.two[, mean((actual - pred)^2)] %>% sqrt`
glm.two = data.table(hour[dteday>=cutoff, .(actual = cnt, dteday)],
                      cas = predict(glm.cas, newdata = hour)[14492:17379],
                      reg = predict(glm.reg, newdata = hour)[14492:17379]) %>%
  .[, pred := cas + reg]
plot_ly(glm.two, x = ~actual, y = ~pred) %>%
  add_markers() %>%
  add_lines(x=c(1,800), y=c(1,800), showlegend = F)
```

*****
```{r, fig.width=8, fig.height=4}
FeatureComparison(list(glm.two$actual, glm.two$pred))
```

*****

* Average hourly prediction
```{r, fig.width=8, fig.height=4}
FeatureDistribution(list(Train = hour[dteday<cutoff,.(dteday, group = "train", value = cnt)],
                         Test = glm.two[,.(dteday, group = "actual", value = actual)],
                         Preds = glm.two[,.(dteday, group = "pred", value = pred)]), 
                    featureName = "value", periodName = "dteday")
```

*****
#### XGB

* `nrounds (# Boosting Iterations)`, `max_depth (Max Tree Depth)`, `eta (Shrinkage)`, `gamma (Minimum Loss Reduction)`, `colsample_bytree (Subsample Ratio of Columns)`, `min_child_weight (Minimum Sum of Instance Weight)`, `subsample`
```{r}
# 10-fold CV
set.seed(seed)
xgb.ctr <- trainControl(method = "repeatedcv", number = 10, repeats = 1)
# search grid
# glm.grd <-  expand.grid(alpha = 0:10/10, lambda = 10^seq(3,-6))
# model                        
set.seed(seed)
xgb.all <- train(x = data.train[,-"cnt"], y = data.train$cnt, 
                 method = "xgbTree", trControl = xgb.ctr)
plot(xgb.all)
```

*****
```{r}
xgb.all$bestTune
```

*****
##### Performance

* RMSE = `r pred.xgb[, mean((actual - pred)^2)] %>% sqrt`
```{r, fig.width=8, fig.height=4}
pred.xgb = data.table(actual = data.test$cnt, 
                      pred = predict(xgb.all, newdata = data.test))
plot_ly(pred.xgb, x = ~actual, y = ~pred) %>%
  add_markers() %>%
  add_lines(x=c(1,800), y=c(1,800), showlegend = F)
```

*****
* In comparison, RMSE on train = `r pred.xgb.train[, mean((actual - pred)^2)] %>% sqrt`
```{r, fig.width=8, fig.height=4}
pred.xgb.train = data.table(actual = data.train$cnt, 
                            pred = predict(xgb.all, newdata = data.train))
plot_ly(pred.xgb.train, x = ~actual, y = ~pred) %>%
  add_markers() %>%
  add_lines(x=c(1,800), y=c(1,800), showlegend = F)
```

*****
#### XGB(2models) {.tabset}

* Slightly performance as the normal XGB model
 
##### Casual model
```{r}
set.seed(seed)
xgb.cas <- train(x = data.train[,-"cnt"], y = cas.train, 
                 method = "xgbTree", trControl = xgb.ctr)
plot(xgb.cas)
```

*****
```{r}
xgb.cas$bestTune
```

*****
##### Registered model
```{r}
set.seed(seed)
xgb.reg <- train(x = data.train[,-"cnt"], y = reg.train, 
                 method = "xgbTree", trControl = xgb.ctr)
plot(xgb.reg)
```

*****
```{r}
xgb.reg$bestTune
```

*****
##### Performance

* RMSE = `r pred.two[, mean((actual - pred)^2)] %>% sqrt`
```{r, fig.width=8, fig.height=4}
pred.two = data.table(hour[dteday>=cutoff, .(actual = cnt, dteday)],
                      cas = predict(xgb.cas, newdata = data.test),
                      reg = predict(xgb.reg, newdata = data.test)) %>%
  .[, pred := cas + reg]
plot_ly(pred.two, x = ~actual, y = ~pred) %>%
  add_markers() %>%
  add_lines(x=c(1,800), y=c(1,800), showlegend = F)
```

*****
```{r, fig.width=8, fig.height=4}
FeatureComparison(list(pred.two$actual, pred.two$pred))
```

*****

* Average hourly prediction
```{r, fig.width=8, fig.height=4}
FeatureDistribution(list(Train = hour[dteday<cutoff,.(dteday, group = "train", value = cnt)],
                         Test = pred.two[,.(dteday, group = "actual", value = actual)],
                         Preds = pred.two[,.(dteday, group = "pred", value = pred)]), 
                    featureName = "value", periodName = "dteday")
```

*****
#### XGB(week)
```{r}
# 10-fold CV
set.seed(seed)
xgb.ctr <- trainControl(method = "repeatedcv", number = 10, repeats = 1)
# search grid
# glm.grd <-  expand.grid(alpha = 0:10/10, lambda = 10^seq(3,-6))
# model                        
set.seed(seed)
xgb.week <- train(x = week.train[,-"cnt"], y = week.train$cnt, 
                 method = "xgbTree", trControl = xgb.ctr)
plot(xgb.week)
```

*****
```{r}
xgb.week$bestTune
```

*****

* RMSE = `r week.xgb[, mean((actual - pred)^2)] %>% sqrt`
```{r, fig.width=8, fig.height=4}
week.xgb = data.table(hour[dteday>=cutoff, .(actual = cnt, dteday)],
                      pred = predict(xgb.week, newdata = week.test))
plot_ly(week.xgb, x = ~actual, y = ~pred) %>%
  add_markers() %>%
  add_lines(x=c(1,800), y=c(1,800), showlegend = F)
```

*****

* Average hourly prediction
```{r, fig.width=8, fig.height=4}
FeatureDistribution(list(Train = hour[dteday<cutoff,.(dteday, group = "train", value = cnt)],
                         Test = week.xgb[,.(dteday, group = "actual", value = actual)],
                         Preds = week.xgb[,.(dteday, group = "pred", value = pred)]), 
                    featureName = "value", periodName = "dteday")
```

*****
#### XGB(date)
```{r}
set.seed(seed)
xgb.date <- train(x = date.train[,-"cnt"], y = date.train$cnt, 
                 method = "xgbTree", trControl = xgb.ctr)
plot(xgb.date)
```

*****
```{r}
xgb.date$bestTune
```

*****

* RMSE = `r date.xgb[, mean((actual - pred)^2)] %>% sqrt`
```{r, fig.width=8, fig.height=4}
date.xgb = data.table(hour[dteday>=cutoff, .(actual = cnt, dteday)],
                      pred = predict(xgb.date, newdata = date.test))
plot_ly(date.xgb, x = ~actual, y = ~pred) %>%
  add_markers() %>%
  add_lines(x=c(1,800), y=c(1,800), showlegend = F)
```

*****

* Average hourly prediction
```{r, fig.width=8, fig.height=4}
FeatureDistribution(list(Train = hour[dteday<cutoff,.(dteday, group = "train", value = cnt)],
                         Test = date.xgb[,.(dteday, group = "actual", value = actual)],
                         Preds = date.xgb[,.(dteday, group = "pred", value = pred)]), 
                    featureName = "value", periodName = "dteday")
```

*****
#### XGB(no mnth)
```{r}
set.seed(seed)
xgb.mth <- train(x = data.train[,-c("mnth","cnt")], y = data.train$cnt, 
                 method = "xgbTree", trControl = xgb.ctr)
plot(xgb.mth)
```

*****
```{r}
xgb.mth$bestTune
```

*****
##### Performance

* RMSE = `r mth.xgb[, mean((actual - pred)^2)] %>% sqrt`
```{r, fig.width=8, fig.height=4}
mth.xgb = data.table(hour[dteday>=cutoff, .(actual = cnt, dteday)],
                     pred = predict(xgb.mth, newdata = data.test))
plot_ly(mth.xgb, x = ~actual, y = ~pred) %>%
  add_markers() %>%
  add_lines(x=c(1,800), y=c(1,800), showlegend = F)
```

*****
* In comparison, RMSE on train = `r mth.xgb.train[, mean((actual - pred)^2)] %>% sqrt`
```{r, fig.width=8, fig.height=4}
mth.xgb.train = data.table(actual = data.train$cnt, 
                           pred = predict(xgb.mth, newdata = data.train))
plot_ly(mth.xgb.train, x = ~actual, y = ~pred) %>%
  add_markers() %>%
  add_lines(x=c(1,800), y=c(1,800), showlegend = F)
```

*****
### Performance

```{r, fig.width=8, fig.height=4}
FeatureDistribution(list(Train = hour[dteday<cutoff,.(dteday, group = "train", value = cnt)],
                         Test = date.xgb[,.(dteday, group = "actual", value = actual)],
                         NoMth = mth.xgb[,.(dteday, group = "pred", value = pred)],
                         XGB2 = pred.two[,.(dteday, group = "pred", value = pred)],
                         Week = week.xgb[,.(dteday, group = "pred", value = pred)],
                         Date = date.xgb[,.(dteday, group = "pred", value = pred)]), 
                    featureName = "value", periodName = "dteday")
```

*****
## Explain
```{r}
#calc.relimp
```

*****
```{r, eval=FALSE}
save.image(file = "bike.RData")
load("bike.RData")
```
